class_docstr: Base class for explicit Runge-Kutta methods.
class_docstr_tok: ["Base","class","for","explicit","Runge-Kutta","methods","."]
code: def __init__(
      self,
      fun,
      t0,
      y0,
      t_bound,
      max_step=np.inf,
      rtol=1e-3,
      atol=1e-6,
      vectorized=False,
      first_step=None,
      **extraneous,
  ):
      warn_extraneous(extraneous)
      super(RungeKutta, self).__init__(
          fun, t0, y0, t_bound, vectorized, support_complex=True
      )
      self.y_old = None
      self.max_step = validate_max_step(max_step)
      self.rtol, self.atol = validate_tol(rtol, atol, self.n)
      self.f = self.fun(self.t, self.y)
      if first_step is None:
          self.h_abs = select_initial_step(
              self.fun,
              self.t,
              self.y,
              self.f,
              self.direction,
              self.order,
              self.rtol,
              self.atol,
          )
      else:
          self.h_abs = validate_first_step(first_step, t0, t_bound)
      self.K = np.empty((self.n_stages + 1, self.n), dtype=self.y.dtype)
  
file_path: catboost/contrib/python/scipy/py2/scipy/integrate/_ivp/rk.py
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from .base import OdeSolver, DenseOutput
  from .common import validate_max_step, validate_tol, select_initial_step, norm, warn_extraneous, validate_first_step
label_desc: Use image node_id 1 to create a new RungeKutta object from inherited base classes: OdeSolver with example: obj = RungeKutta(fun, t0, y0, t_bound, max_step, rtol, atol, vectorized, first_step)